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Deep Learning-Based AI Model for Brain Tumor Segmentation in Digital Pathology and Terahertz Imaging

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dc.contributor.authorOh, Seung Jae-
dc.contributor.authorBark, Hyeon Sang-
dc.contributor.authorMaeng, Inhee-
dc.contributor.authorKang, Chul-
dc.contributor.authorKang, Seok-Gu-
dc.contributor.authorRyu, Han-Cheol-
dc.contributor.authorKim, Se Hoon-
dc.contributor.authorJi, Young Bin-
dc.date.accessioned2026-05-15T02:47:50Z-
dc.date.available2026-05-15T02:47:50Z-
dc.date.created2026-04-29-
dc.date.issued2025-06-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/212315-
dc.description.abstractThis study presents a deep learning-based AI model for brain tumor segmentation in digital pathology images. Using a transgenic mouse model and H&E-stained images, we developed and trained the model with DEEP:PHI, employing U-Net and attention U-Net architectures. The AI model facilitates accurate cancer detection, contributing to terahertz imaging-based diagnostics and enhancing real-time surgical decision-making with minimal pathologist intervention. © 2025 The Author(s)-
dc.language영어-
dc.publisherOptical Society of America-
dc.relation.isPartOfEuropean Conference on Biomedical Optics, ECBO 2025-
dc.titleDeep Learning-Based AI Model for Brain Tumor Segmentation in Digital Pathology and Terahertz Imaging-
dc.typeArticle-
dc.contributor.googleauthorOh, Seung Jae-
dc.contributor.googleauthorBark, Hyeon Sang-
dc.contributor.googleauthorMaeng, Inhee-
dc.contributor.googleauthorKang, Chul-
dc.contributor.googleauthorKang, Seok-Gu-
dc.contributor.googleauthorRyu, Han-Cheol-
dc.contributor.googleauthorKim, Se Hoon-
dc.contributor.googleauthorJi, Young Bin-
dc.identifier.doi10.1364/ECBO.2025.M3A.11-
dc.identifier.urlhttps://opg.optica.org/abstract.cfm?URI=ECBO-2025-M3A.11-
dc.contributor.affiliatedAuthorOh, Seung Jae-
dc.contributor.affiliatedAuthorMaeng, Inhee-
dc.contributor.affiliatedAuthorKang, Seok-Gu-
dc.contributor.affiliatedAuthorKim, Se Hoon-
dc.identifier.scopusid2-s2.0-105031914906-
dc.identifier.bibliographicCitationEuropean Conference on Biomedical Optics, ECBO 2025, 2025-06-
dc.identifier.rimsid92618-
dc.type.rimsART-
dc.description.journalClass1-
dc.description.journalClass1-
dc.type.docTypeConference paper-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
Appears in Collections:
1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers

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